Making HIV testing work at the point of care in South Africa: a qualitative study of diagnostic practices
Bibliographic record
Abstract
BACKGROUND: Point of care testing promises to reduce delays in diagnosing and initiating treatment for infectious diseases such as Human Immuno-deficiency Virus (HIV). In South Africa, decentralized HIV testing with rapid tests offers important lessons for point of care testing programs. Yet, little is known about the strategies of providers and clients to make HIV testing successful in settings short of equipment, human resources and space. We aimed at examining these strategies. METHODS: This paper is based on a larger qualitative study of diagnostic practices across major diseases and actors in homes, clinics, communities, hospitals and laboratories in South Africa. We conducted 101 semi-structured interviews and 7 focus group discussions with doctors, nurses, community health workers, patients, laboratory technicians, policymakers, hospital managers and manufacturers between September 2012 and June 2013 in Durban, Cape Town and Eastern Cape. The topics explored included diagnostic processes and challenges, understanding of diagnosis, and visions of ideal tests. For this paper, the data on HIV testing processes in clinics, communities and hospitals was used. RESULTS: Strategies to make HIV testing work at point of care involve overcoming constraints in equipment, spaces, human resources and workload and actively managing diagnostic processes. We grouped these strategies into subthemes: maintaining relationships, adapting testing guidelines and practices to stock-outs, to physical space, and to different clients, turning the test into a tool to reach another aim and turning the testing process into a tool to enhance adherence. These adaptive strategies are locally negotiated solutions, often ad-hoc, depending on personal commitment, relationships, human resources, physical space and referral systems. In the process, testing is redefined and repurposed. Not all of these repurposing acts are successful in ensuring a timely diagnosis. Some lead to disruptions, unnecessary testing or delays with at times unclear implications for quality of diagnosis. CONCLUSION: Tests shape relationships, professional roles and practices of users at point of care. At the same time, testing processes are dynamic and test results and processes take on new meanings for clients and providers. These insights are crucial for understanding the contexts within which diagnostic devices and policies need to function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".